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Psychol Mark. 2021;1–21. wileyonlinelibrary.com/journal/mar © 2021 Wiley Periodicals LLC
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Received: 2 April 2020
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Revised: 31 March 2021
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Accepted: 8 April 2021
DOI: 10.1002/mar.21496
RESEARCH ARTICLE
The role of brand in overcoming consumer resistance to
autonomous vehicles
Riza Casidy
1
|Marius Claudy
2
|Sven Heidenreich
3
|Efe Camurdan
2
1
Department of Marketing, Macquarie
Business School, Macquarie University,
Macquarie Park, New South Wales, Australia
2
Department of Marketing, Michael Smurfit
School of Business, University College Dublin,
Co Dublin, Ireland
3
Department of Marketing, Faculty of Human
and Business Sciences, Technology and
Innovation Management, Saarland University,
Saarbrücken, Germany
Correspondence
Riza Casidy, Department of Marketing,
Macquarie Business School, Macquarie
University, 4 Eastern Rd, Macquarie Park,
NSW 2109, Australia.
Email: riza.casidy@mq.edu.au
Abstract
Consumer resistance is a major barrier to diffusing radical innovation into mainstream
markets. While recent studies have highlighted the influence that brands might have on
innovation adoption decisions, surprisingly little is understood about the role of brands in
overcoming consumer resistance to radical innovation such as Artificial Intelligence (AI)
technology. To address this, we investigate consumer resistance to AI‐powered tech-
nology in the context of autonomous vehicles. Specifically, this study builds on the self‐
brand connection and brand concept literature to examine: (1) the mechanism through
which self‐brand connection influences radical innovation adoption by mitigating con-
sumer resistance; (2) how this mechanism is moderated by the brand concept (con-
servation vs. openness); and (3) how brands can effectively overcome consumer
resistance through marketing communications. By means of three empirical studies, we
find that self‐brand connections are positively associated with intentions to adopt radical
innovation and that this effect is mediated by reduced risk barriers (Study 1). We further
demonstrate that the influence of self‐brand connection is much lower for conservation
brands than for openness brands (Study 2). Finally, we show that conservation brands
can leverage consumers' self‐brand connection when they effectively enhance their in-
novation capabilities through marketing communications (Study 3).
KEYWORDS
artificial intelligence, autonomous vehicles, brand concept, innovation, self‐brand connection
“… transportation is on the verge of the most significant
transformation since the introduction of the automobile.
Automated or self‐driving vehicles are about to change
the way we travel and connect with one another.”
Elaine L. Chao, U.S. Secretary of Transportation
(remarks prepared for delivery at Detroit Auto Show,
January 14, 2018).
1|INTRODUCTION
Rapid advances in artificial intelligence (AI) have made autonomous
vehicles (AVs) a reality that is likely to radically transform trans-
portation in the near future (Li et al., 2018), making AVs possibly one
of the most disruptive innovations in the automotive industry
(Talebian & Mishra, 2018). AVs thus constitute a radical innovation,
which is defined as the commercialization of an entirely novel idea
that has the potential to significantly disrupt industries and con-
sumer markets (Garcia & Calantone, 2002). In contrast to incre-
mental innovations such as minor product extensions, improvements,
or upgrades, radical innovations bring major changes to firms and
their markets. They are the essence of “creative destruction”
(Schumpeter, 1934) and can lead to the emergence of entirely new
markets (Ritala & Hurmelinna‐Laukkanen, 2013). Although radical
innovation can provide significant benefits to potential users, it also
engenders greater risks and uncertainty (Colombo et al., 2017) and
often meets high levels of resistance from consumers (König &
Neumayr, 2017). While the literature has provided rich insight into
consumer resistance to radical innovation, two limitations call for
further investigation.
First, previous studies have explored in depth how consumers'
evaluation of product characteristics (Mani & Chouk, 2017) and
design features (Verganti, 2008) of radical innovations relates to the
degree of consumer resistance (e.g., Bunduchi, 2017; Joachim et al.,
2018; Norman & Verganti, 2014). Consumer resistance is defined as
the rejection that “is offered by consumers to an innovation, either
because it poses potential changes from a satisfactory status quo or
because it conflicts with their belief structure”(Ram & Sheth, 1989,
p. 6). Consumer resistance thus stems from perceived barriers that
consumers encounter when presented with radical innovation, and
overcoming resistance to innovation represents a critical challenge
for firms when launching and diffusing radical innovation into con-
sumer markets (Claudy et al., 2015; Heidenreich & Kraemer, 2015;
Joachim et al., 2018; Kleijnen et al., 2009; Koch et al., 2020). One
crucial decision that firms need to make when introducing innova-
tions is whether to launch them under existing or new brands (e.g.,
Aaker & Keller, 1990). Although a corpus of studies has explored the
role of brands in launching radical innovations (e.g., Ambler & Styles,
1997; Klink & Athaide, 2010; Smith & Park, 1992), the impact of
brands on consumer resistance remains inconclusive and more re-
search is needed to understand the mechanisms that explain the
brand–consumer resistance relationship in the context of radical
innovations (e.g., Brexendorf et al., 2015; Truong et al., 2017).
Second, researchers have explored how radical innovations
launched by competing brands impact category representations
(Bagga et al., 2016) and consumer–brand relationships for in-
cumbent brands (Lam et al., 2010). However, we know less about
whether established brands can leverage existing
consumer–brand relationships when radical innovations are
launched in consumer markets (Brexendorf et al., 2015;Chen
et al., 2018). Research on incremental innovation shows that
brands that have established strong self‐brand connections
(Escalas & Bettman, 2003)canleveragethesewhenupgradesor
product‐line extensions are launched (Butcher et al., 2019;
Dagogo‐Jack & Forehand, 2018; Magnoni & Roux, 2012). Yet, in
the context of radical innovation, case‐based research suggests
that not all brands are equally well equipped to leverage their
customers' self‐brand connections (Beverland et al., 2010).
Unlike incremental innovation, radically new products require
consumers to accept significant changes not only to products but
also to habits, routines, or entrenched norms and traditions
(Garcia et al., 2007). More importantly, these changes might
conflict with the existing brand concept, that is, the associations
and meanings that indicate what a brand represents (Park et al.,
1986,1991). For example, radical changes introduced to car
manufacturer Rover in the late 1990s led to a decline in sales
because “the gap between Rover's then consumer‐based brand
meaning and the launch of innovative products at the top end of
the market was too great”(Beverland et al., 2010,p.34).The
example highlights that the effects of existing consumer–brand
relationships on radical innovation adoption may be contingent
on the brand concept and, as such, deserves further empirical
investigation (Beverland et al., 2010; Brexendorf et al., 2015).
In this study, we aim to address the two research gaps discussed
above in the context of AVs. By building on the self‐brand connection
literature (Park et al., 2010), we extend prior research (Butcher et al.,
2019; Dagogo‐Jack & Forehand, 2018; Magnoni & Roux, 2012) and
we show that higher connection to brands is associated with lower
resistance to radical innovations, because of lower perceived risk
barriers (Study 1). More importantly, in Study 2 we show that the
strength of this relationship depends on the brand's concept (Torelli
et al., 2012), such that it is significantly weaker for brands with
conservation (vs. openness) concepts. In a third study, we show that
conservation brands can leverage self‐brand connections more ef-
fectively and reduce consumer resistance by making their innovation
capabilities salient through marketing communications (Heidenreich
& Kraemer, 2016).
The remainder of this study is structured as follows. First, we
discuss the empirical context of AVs and why this is an appropriate
setting for examining the role of brands in overcoming resistance to
radical innovation. Next, we draw on the innovation resistance, self‐
brand connection, and brand concept literature to develop our hy-
potheses. We then discuss the methodology and data analytical steps
that were taken to test our hypotheses. Finally, we present the
findings of the three studies and discuss their theoretical and man-
agerial implications.
2|EMPIRICAL CONTEXT: AUTONOMOUS
VEHICLES
Automation using AI‐powered technology is increasingly applied in
diverse platforms such as medical devices and automotive vehicles.
Yet there is still a high degree of skepticism in society regarding the
acceptance of this technology (Hengstler et al., 2016). While many
drivers are already accustomed to lower‐level autonomous driving
technologies such as lane keeping, self‐parking functionalities, and
autonomous emergency braking, fully AVs provide a higher‐level
automated driving system that controls all dynamic aspects of the
driving task under all roadway and environmental conditions (SAE,
2018). More importantly, AVs will significantly disrupt travel beha-
vior and radically transform how people utilize the time they spend
in cars (e.g., Maurer et al., 2016). Analysts also predict a radical
transformation of the car industry. For example, the utilization of
AVs is likely to render obsolete the current models of ownership and
vehicle management, as car companies might be increasingly forced
to focus on offering “ride‐share services using no frills vehicles de-
signed for many years of continuous use”(Finn, 2018). Consequently,
AVs are a prime example of radical innovations that will most likely
result in significant disruptions at consumer, market, and societal
levels (Garcia & Calantone, 2002).
AVs, like other radical innovations, are likely to encounter con-
sumer resistance due to the great level of risks associated with the
technology (Ernst & Young, 2017). For example, accidents involving
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CASIDY ET AL.
AVs have caused fatalities (Tesla Deaths, 2020), raising questions
about the readiness of the technology, as some accidents were
caused by failure to perform simple tasks such as avoiding obstacles
(Cuzzolin et al., 2020). For example, the autopilot system of Tesla
Model S failed to apply the brake during a fatal accident in central
Florida as it could not detect the presence of a tractor trailer “due to
a brightly lit sky”(Tesla, 2016). Indeed, research shows that con-
sumers' willingness to pay for autonomous technologies is in decline
(Deloitte, 2017). Likewise, the academic literature, although limited,
has focused on factors that influence customers' willingness to adopt
driverless cars. For example, Kaur and Rampersad (2018, p. 87)
found that “the ability of the driverless car to meet performance
expectations and its reliability were important adoption determi-
nants.”Kyriakidis et al. (2015) show that consumers are mainly
concerned about the privacy and security threats associated with AV
technology.
While these studies have played pivotal roles in identifying the
perceived technological advantages and risks associated with AV,
they have largely ignored the important dimension of automobile
brands. Indeed, cars are lifestyle products, and brands play an im-
portant role in consumer decision‐making regarding this type of
product (Landwehr et al., 2012). One critical question for established
car manufacturers is how they can leverage their existing brands to
effectively overcome consumer resistance to AV. Anecdotal evidence
from the electric‐vehicle market suggests that established brands
might be at a disadvantage in terms of radical innovation, with recent
media headlines showing that in 2019, Tesla outsold Mercedes‐Benz
for the first time in the US market (Dobush, 2018). In conclusion, the
AV market is highly relevant in terms of both examining consumer
resistance and the importance of brands in consumers' adoption
decisions, making it an ideal research context for our study.
3|THEORETICAL BACKGROUND
3.1 |Resistance to radical innovation
Consumer resistance is one of the biggest hurdles that firms need to
overcome when launching and diffusing radical innovations in con-
sumer markets (Claudy et al., 2015; Heidenreich & Kraemer, 2016;
Joachim et al., 2018; Kleijnen et al., 2009; Ram & Sheth, 1989).
Radical innovations require consumers to accept significant changes,
as they can create great uncertainties and risks (Garcia et al., 2007).
As a result, radical innovations regularly evoke strong negative re-
actions from consumers (Heidenreich & Talke, 2020). Consumer re-
sistance to innovation can manifest in the rejection of a radically new
product by a large number of potential adopters (e.g., Kleijnen et al.,
2009), which can result in market failure and potentially detrimental
consequences for the firm (e.g., Castellion & Markham, 2013;
Heidenreich & Kraemer, 2015).
Research on radical innovation has traditionally been classified
into research on innovation adoption (Rogers, 1962) and on con-
sumer resistance to innovation (Ram & Sheth, 1989). Both paradigms
assume that innovation adoption decisions are the outcome of a
cognitive process “through which an individual …passes from first
knowledge of an innovation, to forming an attitude toward the in-
novation, to a decision to adopt or reject, to implementation of the
new idea, and to confirmation of this decision”(Rogers, 1962 p. 170).
The stage in which consumers form an attitude is generally referred
to as the persuasion stage, as consumers' evaluations of innovation
characteristics result in the formation of negative or positive atti-
tudes, which subsequently determine their decision to adopt or re-
ject a radically new product (Claudy et al., 2015; Joachim
et al., 2018).
Where these two paradigms differ is in regard to the product‐
related antecedents that feature in consumers' evaluations and
subsequently determine their attitudes and adoption intentions. For
example, Rogers' (1962) seminal work showed that innovation
adoption decisions are largely driven by consumers' evaluation and
beliefs regarding five product attributes (i.e., relative advantage;
compatibility; complexity; trialability; and observability). On the
contrary, innovation resistance scholars have shown that consumers'
decision to reject an innovation is shaped by their encounter with
functional and psychological barriers (Bagozzi & Lee, 1999; Claudy
et al., 2015; Joachim et al., 2018; Ram & Sheth, 1989). Although
some have argued that these factors constitute two sides of the
same coin (Herbig & Day, 1992), growing evidence from social psy-
chology and innovation studies (Westaby & Fishbein, 1996; Westaby,
2005; Westaby et al., 2010) shows that the cognitions (e.g., beliefs or
reasons) that underlie consumers' decisions to adopt or reject a ra-
dical innovation are not mere opposites (Claudy et al., 2015). For
example, reasons for rejecting AVs such as safety concerns are
idiosyncratic factors that are unlikely to constitute the logical op-
posite of reasons for adoption, i.e. consumers are unlikely to reject an
AV because it is too safe. Furthermore, findings from psychology
(e.g., loss aversion; Tversky & Kahneman, 1974) and innovation re-
search (Claudy et al., 2015) show that reasons against adoption often
have a greater influence on consumers' attitude formation than
factors that lead to adoption.
Specifically, research suggests that resistance occurs as a result
of various functional and psychological barriers that consumers can
associate when encountering a radical innovation (Claudy et al.,
2015; Heidenreich et al., 2016; Joachim et al., 2018; Ram & Sheth,
1989). Functional barriers arise when a consumer perceives a radical
innovation as “dysfunctional or inadequate for his or her personal
needs and usage expectations, while psychological barriers occur when
an “innovation conflicts with a consumer's social norms, values, or
individual usage patterns, or if its usage is perceived as being too
risky”(Talke & Heidenreich, 2014, p. 899). The literature has recently
identified nine functional and eight psychological barriers that con-
sumers potentially associate with an innovation (Joachim et al.,
2018); however, psychological barriers related to risk and usage are
by far the most widely cited antecedents of resistance to radical
innovation (e.g., de Bellis & Johar, 2020; Salonen, 2018).
Risk barriers refer to the functional, personal, economic, or so-
cial risks and uncertainties that consumers associate with radical
CASIDY ET AL.
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innovation (Joachim et al., 2018). For example, consumers might
resist AVs due to fears of technological malfunction that would
compromise their safety (Shariff et al., 2017). Usage barriers relate
to consumers' beliefs that a radical innovation constitutes an un-
desirable disruption to established usage patterns, habits, routines,
or lifestyles (Ram & Sheth, 1989). In the context of AVs, consumers
might resist because AVs take away the joy of driving or because of
their unwillingness to hand over control to a nonhuman entity
(Atasoy & Morewedge, 2018).
However, the relevance and magnitude of these barriers in the
context of AVs remain an empirical question. For example, Joachim
et al. (2018) found that “the effect sizes of each barrier …vary with
the context present”(p. 105), thereby adding to a growing evidence
base that suggests that the relative effect of these antecedents is
highly context‐dependent (Claudy et al., 2015). Indeed, findings show
that the degree and relative importance of innovation barriers de-
pend on the type of innovation (e.g., Antioco & Kleijnen, 2010;Ma
et al., 2015), the firm (e.g., Sinapuelas et al., 2015), or the product
category (e.g., Berger & Nakata, 2013; Claudy et al., 2013), to name
but a few.
Although this body of research has provided rich and useful in-
sights into the factors that determine the strength of the relationship
between perceived barriers and innovation resistance, less is un-
derstood about how brands influence consumers' evaluations of ra-
dical innovations (Aboulnasr & Tran, 2019; Brexendorf et al., 2015;
Nedergaard & Gyrd‐Jones, 2013). Brands are, arguably, any com-
pany's greatest asset, and evaluations of radical innovation launched
by a brand are likely to be influenced by consumers' knowledge,
perceptions, and relationship with the brand (Besharat, 2010).
Hence, brands, radical innovations, and consumer resistance to in-
novation seem to be intertwined. However, empirical findings on the
relationship between consumer resistance and brands are scarce.
Most research in the area is concerned with resistance to brands
as opposed to innovation (for an overview see Table 1), focusing on
the conceptualization of brand avoidance (Albinsson et al., 2010;
Cambefort & Roux, 2019; Lee et al., 2009), as well as factors that
drive negative reactions to new brands or brand extensions (Oakley
et al., 2008). Other studies have investigated the consequences of
consumers' negative reactions to brands (Curina et al., 2020).
However, few studies have investigated innovation resistance in
brand contexts, and whether and how brands influence consumer
evaluation of radical innovations and their subsequent adoption
decisions. For example, Füller et al. (2013) suggest that brands can
be used to increase awareness of an innovation, to communicate its
benefits, and to reduce innovation resistance. Likewise, prior re-
search in the context of incremental innovation has shown that
brand strength is positively associated with perceived product
quality (Dawar & Parker, 1994; Page & Herr, 2002) as well as with
consumers' proclivity to adopt incrementally new products
(Corkindale & Belder, 2009; Gielens & Steenkamp, 2007), suggesting
that brands may be effective in alleviating negative effects of in-
novation resistance. However, other studies show that innovation
resistance in brand contexts is driven by the same barriers as
resistance in new product contexts (Chen et al., 2018). Furthermore,
some studies have even shown that some consumers evaluate in-
novations more favorably when they are launched under new as
opposed to existing brand names (e.g., Klink & Athaide, 2010). In light
of this scant and inconclusive evidence, more research is needed to
investigate the mechanisms and boundary conditions that explain the
relationship between brands and consumer resistance in contexts of
radical innovations (e.g., Brexendorf et al., 2015; Truong et al., 2017).
For consumers, brands represent more than just product func-
tions or benefits (Fournier, 1998; Park et al., 2010). Consumers often
form strong cognitive as well as emotional connections with brands,
which in time turn into relationships (e.g., Fournier, 1998; Keller,
2012; McAlexander et al., 2002; Park et al., 2010). Hence, such self‐
brand connections seem effective in overcoming consumer re-
sistance and enhance the adoption of radically new products. How-
ever, empirical evidence for this proposition is still missing. In the
following we thus draw on self‐brand connection literature to in-
vestigate how the connections that consumers form with brands
might impact their resistance to radical innovations that are being
introduced by these brands. Table 2provides a definition of the key
constructs used in this study, which will be elaborated in the next
section.
3.2 |Self‐brand connection and consumer
resistance to innovation
Self‐brand connections reflect the extent of cognitive and emotional
ties that exist between a consumer and a brand (Escalas & Bettman,
2003). Self‐brand connection can manifest in strong and complex
feelings that consumers experience towards the brand, which dif-
ferentiate the self‐brand connection concept from other constructs
such as brand attitudes (Park et al., 2010). Whereas strong brand
attitudes are associated more with confidence in the brand on the
basis of brand judgment, strong self‐brand connection relates to
one's relationship with the brand and the linkages between what the
person and the brand represent (Park et al., 2010).
The literature has provided strong empirical support for the in-
fluence of self‐brand connection in brand relationships, and findings
show that it constitutes a critical determinant of brand attachment
and brand equity (Liu et al., 2012; Mazodier & Merunka, 2012; Park
et al., 2010). Research also shows that consumers are more likely to
be attracted and committed to brands that are consistent with their
self‐concept (Bhattacharya & Sen, 2003; Mulyanegara et al., 2009).
Consumers with strong self‐brand connection see parts of their
identity reflected in the brand and, consequently, are more likely to
defend the image of the brand when it is under threat, mainly be-
cause they regard protecting the brand image as protecting their
own self‐concept (Cheng et al., 2012).
Radical innovations present a potential threat to a brand, be-
cause they constitute a significant departure from the status quo and
thus carry a high risk of failure, which could adversely affect the
brand image. For example, the possibility of malfunctioning
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technology that could lead to serious or even fatal accidents caused
by an AV represents a substantial threat to a brand. However,
consumers with strong self‐brand connections would apply a defense
mechanism that would result in lower innovation resistance and
consequently lead to a greater probability of adopting the innova-
tion. Empirical evidence from prior studies seems to support this
prediction. For example, Swaminathan et al. (2007) found that con-
sumers with strong self‐brand connections tend to discount and
counterargue negative information about the brand. Cheng et al.
(2012) demonstrate that consumers with strong self‐brand connec-
tion are likely to experience more distress when presented with
negative information about the brand, and are more likely to engage
in defense mechanisms that would see the image of the brand (and
thus oneself) being restored. Ferraro et al. (2013) found that con-
sumers with strong self‐brand connection maintain their favorable
view of the brand in the face of conspicuous brand users, while
consumers with low self‐brand connection exhibit less favorable at-
titudes towards both brand users and the brand. In our context,
TABLE 1 Results of literature review on brands and innovation resistance
Author (Year) Focus of investigation Main findings
Cherrier (2009) Concept of brand resistance Identification of two types of resistant consumer identities: the hero
identity (resistance against exploitative consumption) and the project
identity (resistance against positional consumption).
Lee et al. (2009) Concept of brand resistance and
determinants
Identification of three types of brand avoidance: (1) Experiential brand
avoidance (driven by unmet expectations), (2) Identity avoidance (driven
by incongruence with the individual's identity), and (3) moral avoidance
driven by consumers' ideological beliefs clashing with certain brand
values or associations).
Lam et al. (2010) Determinants of resistance to brand
switching
Confirmation of inhibitory effects of customers' identification with and
perceived value of the incumbent relative to the new brand (radical
innovation) on switching behavior.
Albinsson et al. (2010) Concept of brand resistance Exploration of Western brand resistance as a kind of resistance to Western
practices of hyper‐consumption, frugality‐as‐an‐ethic, and an aversion to
low‐quality throwaway‐type products.
de Kervenoael
et al. (2011)
Determinants of brand resistance Brand resistance results from an evaluation of functionality (e.g.,
convenience, value for money, refund policy), intuitive factors (e.g., style,
image, quality), and broader processes of consumption from parental
boundary setting (e.g., curbing premature adultness).
Füller et al. (2013) Brands as lever to enhance diffusion (and
overcome innovation resistance)
The costless creation of strong brands by consumers as a side‐effect of
normal community functioning helps in convincing potential adopters
and thus enhances diffusion of user‐developed innovations.
Rindell et al. (2014) Determinants of brand resistance Consumers may reject brands based on two factors leading to brand
avoidance: persistency (persistent vs. temporary) and explicitness
(explicit vs. latent).
Lee and Lee (2017) Concept of brand resistance Confirmation of cultural differences in brand resistance, such that Asian
consumers develop less cognitive dissonance upon low similarity
extensions than Western consumers. Western consumers experience
much stronger resistance to unexpected brand extension information
than Asian consumers.
Baek and Kim (2018) Determinants of resistance to brand
switching
Commitment and resistance to innovation inhibit switching intentions
among brands.
Cambefort and
Roux (2019)
Concept of brand resistance and
determinants
Brand resistance can take different intensity levels, from avoidance to
offline and online word‐of‐mouth, through boycott and activism to
extreme acts, depending on four types of risks: (1) performance (lack of
suitable alternatives for the brand), (2) social issues (stigma and
exclusion), (3) legal reasons (legal proceedings), or (4) physical
considerations (violation of physical integrity).
Chen et al. (2018) Types and determinants of brand
innovation resistance
Identification of three types of resistance to brand app innovations:
postponement, opposition, and rejection, driven by value, image, and
usage barriers.
Curina et al. (2020) Consequences of brand resistance Brand hate enhances offline negative word‐of‐mouth, online complaining,
and non‐repurchase intention.
CASIDY ET AL.
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consumers with strong self‐brand connections would be expected to
protect the brand (and therefore their own self‐concept) by evalu-
ating a radical innovation more positively and thereby maintaining
their favorable view of the brand.
Furthermore, consumers with strong self‐brand connections are
likely to associate lower risk and uncertainty with a radical innova-
tion launched by the brand. As discussed earlier, perceived risk re-
flects the functional, personal, economic, or social uncertainties
consumers associate with adopting a radical innovation (Joachim
et al., 2018). Self‐brand connections entail experience, familiarity,
and trust, which is expected to reduce consumer resistance to ra-
dically new products (e.g., Truong et al., 2017). Building on these
findings, we expect that strong self‐brand connections are associated
with a greater propensity to adopt a radical innovation, because they
mitigate risk and usage barriers that lead to innovation resistance.
Formally:
H1–H2: Consumers' self‐brand connections are positively associated
with their intentions to adopt a radical. innovation (H1), be-
cause of lower perceived risk and usage barriers that are re-
lated to innovation resistance (H2).
3.3 |The moderating influence of brand concept
If our predictions hold true, the question arises: Can all brands
leverage their customers' self‐brand connections to overcome re-
sistance to radical innovations? Research from adjacent domains
might suggest otherwise. In a qualitative study, Beverland
et al. (2010) found that firms' ability to successfully launch new
products was somewhat contingent on the brand's concept. A brand
concept is a unique set of associations and meanings that indicate
what the brand represents (Park et al., 1986,1991). Brand concepts
therefore occupy unique positions in the minds of consumers. For
example, Whole Foods is associated with organic, wholesome food,
and hence connotes quality. Rolex is associated with prestige,
whereas The Body Shop is associated with ethical production (Park
et al., 1991).
In establishing these concepts, brands often relate themselves to
human traits and values (Torelli et al., 2012). One of the most widely
established frameworks that is used to build and evaluate brand
concepts is Schwartz's (1992)human values theory, which has been
empirically tested and supported in over 200 studies in more than
70 countries (Schwartz & Rubel, 2005). Schwartz's theory organizes
human values in a circular fashion, consisting of four higher‐order
categories: self‐transcendence; self‐enhancement; openness to
change; and conservation. Values that are diametrically opposed to
each other (e.g., self‐enhancement vs. transcendence) constitute
motivational conflicts. For example, the pursuit of self‐enhancement
values like achievement and power is likely to conflict with the
transcendent values of benevolence and equality (Schwartz, 1992).
On the other hand, values adjacent to each other (e.g., self‐
enhancement and openness) are compatible and can therefore be
pursued simultaneously.
In the context of radical innovation, we are particularly inter-
ested in the notion of change, which is best captured by the dia-
metrically opposed conservation and openness values. According to
the literature, conservation brand concepts represent tradition and
conformity, which signal preservation of the status quo, while
openness concepts suggest excitement and novelty and, hence, signal
openness to change (Torelli et al., 2012). When introducing radical
innovation, brands that are characterized by a conservation concept
TABLE 2 Construct definition
Construct Source/Reference Definition
Self‐brand
Connection
Escalas and Bettman (2003, p. 340) The extent to which a consumer has incorporated a brand into
his/her self‐concept.
Innovation
resistance
Ram and Sheth (1989, p. 6) Rejection offered by a consumer to a radical innovation, either
because it poses potential changes from a satisfactory
status quo or because it conflicts with his/her belief
structure.
Risk barrier For example, Joachim et al. (2018), Kleijnen
et al. (2009), Ram and Sheth (1989), and Talke and
Heidenreich (2014)
The degree to which a consumer believes that a radical
innovation poses a functional, personal, economic, or social
risk to him/her.
Usage barrier For example, Joachim et al., 2018; Molesworth & Suortti,
2002; Ram & Sheth, 1989; Talke & Heidenreich (2014)
The degree to which a consumer believes that a radical
innovation disrupts established use patterns, habits,
routines, or lifestyles.
Radical innovation
adoption
Arts et al. (2011) The desire expressed by a consumer to try, use, or purchase a
radical innovation in the near future.
Brand concept Park et al. (1986) The associations and meanings that indicate what a brand
represents.
Innovation capability Wang and Ahmed (2007) A consumer's perception of a firm's ability to develop novel
innovation.
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CASIDY ET AL.
might experience higher levels of resistance because of their posi-
tioning along the dimensions of tradition and conformity, thereby
making the launch of radical innovations such as AV seem riskier.
Conversely, brands that are characterized by an openness concept
might experience lower level of resistance as they are positioned
along the dimensions of excitement and novelty (Torelli et al., 2012).
More importantly, we would expect the hypothesized effect of
self‐brand connection on radical innovation adoption to be weaker
for brands with conservation (vs. openness) concepts. In other words,
we expect the strength of the relationship between self‐brand con-
nection and radical innovation adoption to be contingent on the
brand concept. Specifically, we would expect consumers who have
strong self‐brand connection with conservation brands (vs. openness
brands) to care more about and identify with the brand's values of
tradition or conformity. Those brand users understand that the
brand concept is mainly about the preservation of the status quo,
which is largely at odds with radical innovation. Prior studies suggest
that “value meanings that are opposed to the existing brand concept
leads consumers to experience a sense of unease or disfluency, which
in turn results in unfavorable evaluations of the message”(Torelli
et al., 2012, p. 96). Further, Zhu et al. (2018) assert that conservation
resonates with traditional values that restrict behaviors that are
inconsistent with social norms. Because AVs are yet to gain full ac-
ceptance in the market due to the risks associated with the tech-
nology, adopting AVs may be considered as going against “social
norms”(Deb et al., 2017) and, as such, would not be favored by
consumers who have strong self‐brand connection with conservation
brands.
Conversely, consumers who strongly identify with openness
brands are more likely to embrace the brand's novelty attributes,
which would give them greater confidence in the brand's ability to
adjust to significant changes brought about by radical innovation.
Indeed, “openness means people are ready to accept changes and to
follow their ability and emotions.
in uncertainties rather than preserve the status quo,”and
openness values “motivate individuals towards exploration, novelty,
and change”(Zhu et al., 2018, p. 735). Brands with openness concept,
therefore, should experience lower levels of innovation resistance as
the departure from status quo inherent in radical innovation is
consistent with the openness brand concept. These explain why
brands recognized as having openness concept (e.g., Apple) seem to
encounter lower resistance when introducing radical innovation (Wu
et al., 2019). Following this reasoning, we would expect consumers
who have strong self‐brand connection with openness brands to
exhibit lower resistance to adopting a radical innovation launched by
the brand. Conversely, strong self‐brand connection would not be as
effective in reducing innovation resistance for conservation brands.
Stated formally:
H3: The positive relationship between self‐brand connections and in-
tentions to adopt a radical innovation is stronger for brands with
openness concepts than for brands with conservation concepts.
3.4 |Overcoming consumer resistance by
communicating innovation capabilities
Our predictions suggest that brands with openness concepts have an
advantage when consumers' self‐brand connections are leveraged
during launches of radical innovations such as AVs. However, we are
also interested in ways for conservation brands to overcome this
disadvantage in the marketplace (Greenblatt, 2016).
A plethora of research shows that marketing communications
influence brand perceptions as well as product evaluations (Buil
et al., 2013; Dwivedi & McDonald, 2018). Furthermore, effective
marketing communications create halo effects that extend from
brands to specific product attributes (Beckwith & Lehmann, 1975;
Boatwright et al., 2008; Klein & Dawar, 2004). For example, brands'
corporate social responsibility activities have been shown to impact
consumers' evaluations of the brand as well as their evaluations of
product performance (Chernev & Blair, 2015). Likewise, health
claims at the brand level have been shown to result in significant
increases in product attribute ratings (Andrews et al., 1998; Roe
et al., 1999).
Building on these findings (Beckwith & Lehmann, 1975;
Boatwright et al., 2008; Klein & Dawar, 2004), we propose that
brands with conservation concepts can lower consumer resistance to
their radical innovations through effective communication of their
innovation capabilities. While conservation brands might find it more
difficult to leverage consumers' self‐brand connections to overcome
resistance to radical innovations, we suggest that using marketing
communications to strengthen consumers' beliefs in the brand's in-
novation capabilities can potentially lower consumer perceptions of
resistance‐related barriers. Therefore, even for conservation brands,
the positive effect of self‐brand connection on adaption intentions
could be maintained through the effective use of marketing com-
munications. Stated formally:
H4: For conservation brands, the effect of self‐brand connections on
adoption intentions is stronger (weaker) when consumers' perceptions of in-
novation capabilities are heightened through marketing communications.
The following section discusses the methodology and empirical
studies to test the focal hypotheses of this study.
4|METHODOLOGY AND RESULTS
4.1 |Overview of studies
We tested our focal hypotheses by means of one pre‐test and three
studies. In the pre‐test, we elicited the most salient barriers that
consumers associate with the adoption of AVs (Westaby et al., 2010).
Open‐ended responses were translated into items, which were uti-
lized in all subsequent studies. In Study 1 (n= 294), we tested the
core premises that consumers' self‐brand connections are positively
related to their intentions to adopt AVs (H1), because they associate
lower risk and usage barriers with the technology (H2). Study 2
CASIDY ET AL.
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7
(n= 288) aimed to test whether the strength of the mediated re-
lationship between self‐brand connections and intentions to adopt
AVs is moderated by the brand concept (H3). In Study 3 (n= 575) we
tested if conservation brands can leverage consumers' self‐brand
connections more effectively by communicating their innovation
capabilities (H4).
For all studies, participants were recruited through MTurk and
Prolific Academic. Importantly, all studies included a screening
question (e.g., do you hold a valid driver license?), and respondents
without a valid driver license could complete the survey but were not
included in the final sample (Wessling et al., 2017). Second, all studies
contained attention check items, and those who failed the attention
check were excluded from the final sample. For the scenario‐based
manipulations, we set a minimum time of 30 s during which re-
spondents were not able to advance in the survey, to ensure that
they take enough time to read the scenario. All studies had received
ethical approval from a major Australian University.
4.2 |Pre‐test
Because the relevance and magnitude of barriers related to con-
sumers' innovation resistance are highly context‐dependent (Claudy
et al., 2015; Joachim et al., 2018), we first elicited the most salient
barriers regarding AVs. Following procedures similar to those of
Claudy et al. (2015) and Westaby et al. (2010), participants (n= 115)
were recruited via MTurk and were asked to answer open‐ended
questions regarding why they would not adopt AVs. Findings were
coded and translated into items. In a second step, scales were refined
and validated with another sample of participants (n= 290). Findings
show that consumers associate predominantly risk (α= 0.86) and
usage barriers (α= 0.79) with AVs (see Tables A1 and A2 in the
Appendix). Regarding risk barriers, consumers were mainly con-
cerned about safety issues and the possibility of technological mal-
function associated with AVs (Salonen, 2018; Shariff et al., 2017). In
terms of usage barriers, consumers were worried about loss of
“freedom”and “joy of driving”, which is consistent with other studies
on consumer resistance to autonomous technology (de Bellis &
Johar, 2020; Schweitzer & Van den Hende, 2016). These two scales
were used in all subsequent studies. Table 3provides details of all
measures that were used in Studies 1–3.
4.3 |Study 1
4.3.1 |Research design
The first study aimed to test the proposed direct (H1) and mediated
(H2) effect of self‐brand connection on adoption intentions. For this
purpose, we selected five established car brands, which were actively
developing AVs at the time of data collection (QY Market Research,
2017). Based on a US sales performance ranking for 2019 (Statista,
2019), Ford (ranked 1st) and Toyota (2nd) were chosen to represent
the market leaders, while Mazda (17th) was chosen to represent the
market follower. BMW (15th) and Tesla (20th) were chosen to re-
present premium car brands.
We recruited 294 participants (M
age
= 36.76, SD = 11.99; 149
female; median income $40,000–$49,999) via MTurk and randomly
assigned them to one of the five car brands. After reading a short
cover story and some warm‐up questions, participants were asked
about their connections with the brand, using Escalas and Bettman's
(2003) self‐brand connection scale (α= 0.95). Specifically, the scale
measures the extent to which respondents believe that the focal
brand is congruent with their self‐concept using a 7‐point Likert
scale (1 –Strongly Disagree, 7 –Strongly Agree). Participants then
read a short fictitious article from a leading car magazine, in which
they were informed that the respective car brand was about to
launch a fully AV. Participants were then asked to answer some
questions in relation to this radical innovation. First, we measured
their resistance by asking them to evaluate risk (five items; α= 0.88)
and usage barriers (three items; α= 0.83). Consistent with ap-
proaches used in prior studies (Claudy et al., 2015; Westaby, 2005),
participants rated the extent to which each of the items represented
a reason for not adopting AVs on a 3‐point scale (1 = not a reason,
2 = a reason, and 3 =a strong reason). Finally, we asked respondents
to state their intentions to adopt (α= .92) AVs launched by this brand
(Barone & Jewell, 2013). Respondents indicated their level of
agreement with statements relating to intention of adopting AV,
using a 7‐point Likert scale (1 –Strongly Disagree, 7 –Strongly
Agree). We also controlled for age, gender, and income (Claudy et al.,
2015; Heidenreich & Spieth, 2013).
4.3.2 |Results and analysis
Before testing the hypothesized relationships, we ran confirmatory
factor analysis (CFA) via Mplus 7.4 to examine the reliability and
validity of the focal constructs. The loadings of each construct were
statistically significant and greater than the cutoff value of 0.50. All
measures showed good reliability and convergent validity as re-
flected in the composite reliabilities (CRs) (>.70) (Bagozzi & Yi, 1988).
Further, as indicated in Table 4, Panel A, the square root of average
variance extracted (AVE) for each construct exceeded the correla-
tions between variables, thus indicating discriminant validity.
To test the research hypotheses, we employed a mediation
analysis (PROCESS Model 4) following the procedure recommended
by Hayes (2017). Self‐brand connection was the predictor, risk and
usage barriers were the mediators, and adoption intention was the
dependent variable. Supporting H1, the effects of self‐brand con-
nection on adoption intentions were significant (β= 0.37, t= 7.35,
p< 0.001). With respect to mediation, we found a significant and
negative effect of self‐brand connection on perceived risk barriers
(β=−0.06, t= 2.72, p< 0.01), which in turn (β
risk
=−0.85, t=−6.17,
p< 0.001) negatively affected consumers' intentions to adopt this
radical innovation. The indirect effect via risk barriers (β
indirect
= 0.05,
95% confidence interval [CI] = 0.0122–0.1012) was significant,
8
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CASIDY ET AL.
providing support for H2. More specifically, in this case, com-
plementary mediation is present, as the signs of the direct and in-
direct effects were both positive. However, the effect of self‐brand
connection on usage barriers was not significant (β=−0.01, t= 0.58,
p> 0.10). The effects of usage barrier on adoption intentions, how-
ever, were significant (β
usage
=−0.51, t=−3.96, p< 0.001). The in-
direct effect via usage barriers was therefore not significant
(β
indirect
=−0.01, 95% CI = −0.0383 to 0.0177]). Figure 1summarizes
the empirical findings of Study 1. Detailed results of the analysis with
the control variables are provided in Appendix A.
4.3.3 |Discussion of results
Our findings provide support for the hypothesis that self‐brand
connections are positively associated with consumers' intention to
adopt radical innovations (H1), because of lower perceived barriers
that are related to innovation resistance (H2). Specifically, we find
that self‐brand connections appear to adversely affect risk barriers,
which subsequently result in lower resistance and higher adoption
intentions. The finding is congruent with Truong et al.'s (2017) pro-
position that brands serve an important function in consumers' risk
reduction strategies when engaging with radically new products. On
the contrary, we did not find support for the effects of self‐brand
connection on usage barriers. In other words, irrespective of self‐
brand connection, people's concerns about reduced joy of driving or
feelings of freedom would still negatively impact their adoption in-
tentions. Next, we examine whether the mediated effect of self‐
brand connection on adoption intentions is moderated by the brand
concept (H3).
4.4 |Study 2
4.4.1 |Research design
To test the moderating role of brand concept (H3), we recruited 288
US respondents (M
age
= 32.14, SD = 11.02; 150 female; median in-
come $50,000–$59,999) from Prolific Academic. We utilized the
same constructs, scenario, and scale items as in Study 1. However, in
this study, we employed a between‐subject design and randomly
assigned respondents to conservation (Ford; n= 145) versus open-
ness (Tesla; n= 143) condition to test whether the effect of self‐
brand connection on adoption intentions is moderated by the brand
concept. A pre‐test (Appendix A) had revealed that Ford rated
highest on the conservation dimension, while Tesla rated highest on
TABLE 3 Psychometric properties
Study 1 Study 2 Study 3
Construct Item SFL tSFL tSFL t
Self‐brand connection The [X] brand reflects who I am 0.883 55.600 0.817 20.934 0.857 61.571
I feel a personal connection to the [X] brand 0.915 70.840 0.843 23.168 0.882 72.302
I use the [X] brand to communicate who I am to other people 0.925 79.347 0.769 17.158 0.917 97.277
I think the [X] brand helps me become the type of person I want
to be
0.916 73.972 0.846 22.536 0.913 93.981
Risk barrier Because the technology is not safe 0.624 15.910 0.728 15.228 0.635 22.853
Because of the possibility of technological failure or malfunction 0.752 25.695 0.784 18.800 0.742 33.825
Because the technology can never replace human judgment in
dangerous situations
0.821 35.196 0.721 14.619 0.825 48.564
Because the technology might not react correctly in ambiguous
situations
0.852 41.130 0.805 20.764 0.817 47.097
Because the technology could never predict other human behavior 0.834 37.611 0.749 16.942 0.817 47.188
Usage barrier Because I do not want to give up control 0.744 20.633 0.696 12.257 0.761 31.285
Because it limits my feeling of freedom when driving 0.862 30.516 0.874 19.254 0.890 46.718
Because it reduces the joy of driving 0.768 24.537 0.708 13.652 0.732 31.294
Radical innovation
adoption
If asked, I am willing to take a test drive in a [X] with an
autonomous driving system today
0.698 21.834 0.875 41.999 0.725 34.920
I can see myself using [X]'s autonomous driving system in the future 0.877 55.121 0.952 94.537 0.910 108.701
I will use [X]'s autonomous driving system in the future. 0.943 97.309 0.971 124.355 0.965 200.427
I will purchase a [X] car with autonomous driving system in the
future
0.927 83.796 0.909 56.602 0.939 152.451
Abbreviation: SFL, standardized factor loading.
CASIDY ET AL.
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9
the openness dimension. We utilized the same scenario as in Study 1,
but changed the respective brand names to Ford (conservation)
versus Tesla (openness). Participants were randomly assigned to one
of the conditions, and asked some general questions regarding their
involvement with the product category and brand familiarity, before
answering questions regarding their self‐brand connections
(α= 0.91). Next, they read about the brand launching a fully auto-
mated car, and then answered questions related to innovation re-
sistance (i.e., risk barriers (α= 0.87) and usage barriers (α= 0.81)) and
adoption intentions (α= 0.96). We also included two manipulation
checks to verify respondents' perception of conservation (e.g., In
your opinion, how traditional is the [X] brand?) and openness (e.g., in
your opinion, how creative is the [X] brand?) of Ford and Tesla.
4.4.2 |Results and analysis
CFA shows that the indicator loadings of each construct are
statistically significant and greater than the cutoff value of 0.50
(Table 3). Each construct showed good reliability and convergent
validity as reflected in the CRs > 0.70 (Bagozzi & Yi, 1988). Fur-
ther, as shown in Table 4, Panel B, the square root of AVE for
each construct exceeds the correlations between variables, thus
confirming discriminant validity.
To test the focal hypothesis, we employed moderated med-
iation analysis (PROCESS Model 7) following the procedure re-
commended by Hayes (2017). Self‐brand connection was the
predictor, risk and usage barriers were the mediators, brand
TABLE 4 Correlation matrix for all
empirical studies
A. Study 1
Mean SD CR1234
1. Self‐brand connection 2.71 1.53 0.95 0.910
2. Risk barrier 2.21 0.62 0.89 −0.153*0.781
3. Usage barrier 1.73 0.65 0.84 0.045 0.489** 0.793
4. Radical innovation adoption 4.26 1.61 0.92 0.473** −0.464** −0.347** 0.867
B. Study 2
Mean SD CR 1 2 3 4
1. Self‐brand connection 2.81 1.44 0.89 0.819
2. Risk barrier 2.20 0.61 0.87 −0.171 0.758
3. Usage barrier 1.69 0.66 0.81 0.071 0.371** 0.764
4. Radical innovation adoption 4.63 1.74 0.96 0.398** −0.454** −0.385** 0.928
C. Study 3
Mean SD CR 1 2 3 4
1. Self‐brand connection 3.14 1.53 0.94 0.893
2. Risk barrier 2.30 0.59 0.88 −0.088 0.771
3. Usage barrier 1.79 0.66 0.84 −0.026 0.552** 0.797
4. Radical innovation adoption 4.37 1.60 0.94 0.375** −0.462** −0.404** 0.890
Note: Values in italics indicate the square root of average variance extracted.
Abbreviations: CR, composite reliability; SD, standard deviation.
*Significant at 0.05 level.
**Significant at 0.01 level.
Self-Brand
Connections
Risk
Barriers
Usage
Barriers
Adoption
Intention
-.064(-.157)** -.853(-.328)**
-.514(-.208)**
.370(.351)**
.015(.034) (R2=.366)
FIGURE 1 Empirical results for Study 1.
*Significant at 0.05 level; **Significant at 0.01
level. Values in brackets indicate standardized
regression coefficients
10
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CASIDY ET AL.
concept (0 = Ford representing conservation brand; 1 = Tesla re-
presenting openness brand) was the moderator. Intentions to
adopt were the dependent variable.
The manipulation check suggests that participants rated the
brand concepts as predicted, with Ford (Tesla) ranking significantly
higher (lower) on conservation and lower (higher) on openness
(MConservation
Ford
=5.84>MConservation
Tesla
=2.41, F(1,286) = 2.03,
ρ<0.001; MOpenness
Ford
=3.47<MOpenness
Tesla
=5.72, F(1,286) =
7.48, ρ<0.001).
Regarding our hypotheses, findings replicate results from Study
1, showing that self‐brand connections are positively associated with
adoption intentions (β= 0.37, t= 6.37, p< 0.001). More importantly,
the indirect effect of self‐brand connections on adoption intentions
via reduced risk barriers is significant in the openness condition
(β
indirect
= 0.13, 95% CI = 0.075–0.200), but not in the conservation
condition (β
indirect
= 0.03, 95% CI = −0.028 to 0.085). The index
of moderated mediation was significant (β= 0.11, 95%
CI = 0.0165–0.2193), providing support for H3. Like in Study 1, self‐
brand connection had no significant impact on usage barriers in ei-
ther the conservation (β=−0.07, t= 1.80, p> 0.05) or openness
condition ( = −0.05, t=−1.27, p> 0.10). Figure 2summarizes the
empirical findings of Study 2.
4.4.3 |Discussion of results
The findings suggest that the mediated relationship between self‐brand
connection and adoption intentions is contingent on the brand concept
(openness vs. conservation). Specifically, we find that self‐brand con-
nections reduce risk barriers for openness brands but not for con-
servation brands. Conservation brands seem unable to leverage
consumers' self‐brand connection to overcome perceived risk barriers,
potentially putting them at a disadvantage when launching radical in-
novations like AVs. The findings thus add to a growing body of research
on the role of brands in innovation adoption decisions, by showing that
brand concept constitutes an important boundary condition to the
brand–consumer response relationship in the context of radical innova-
tions (Brexendorf et al., 2015;Truongetal.,2017).
4.5 |Study 3
4.5.1 |Research design
Study 3 aimed to explore whether conservation brands can also
leverage consumers' self‐brand connections to reduce risk barriers,
and thus overcome resistance to radical innovations. Specifically, we
wanted to test the role of perceived innovation capabilities as a
potential moderator to the self‐brand connection –consumer re-
sistance relationship.
To test whether conservation brands can communicate their
innovation capabilities to strengthen the relationship between self‐
brand connections and innovation adoption, we employed a
between‐subjects design in which participants were randomly as-
signed to a high versus low innovation capability condition. 575
participants (M
age
= 37.82, SD = 11.28; 287 female; median income
$50,000–$59,999) were recruited via MTurk.
To add to the external validity and generalizability of our find-
ings, in this study we chose a different brand (i.e., Toyota), which had
also scored high on the conservation concept (M= 4.83) in our pre‐
test results (see Table A3). We used the same survey instructions
and items as in Studies 1 and 2, asking consumers to first indicate
their connections with the Toyota brand (α= 0.94). However, before
consumers answered questions related to radical innovation, they
were randomly assigned to the high versus low innovation capability
conditions. Following an approach similar to that of Gürhan‐Canli
and Batra (2004), respondents were asked to read a public relations
excerpt in a periodical (Fortune magazine) in which experts ranked
automotive brands according to their innovation capabilities. In the
high innovation capability condition, participants were told that
Toyota ranked second in Fortune's“Most Innovative Car Brands“
survey. In the low innovation capability condition, participants were
told that Toyota ranked sixth in the survey (see Appendix A). The
manipulation check for the effectiveness of the marketing commu-
nication was evaluated using a three‐item measure (α= 0.87) from
Barone and Jewell (2013) (i.e., The Toyota brand is: innovative,
contemporary, and a leader). Using the same scales as in Studies 1
and 2, participants then evaluated usage barriers (α= 0.83) and risk
Self-Brand
Connection
Risk
Barriers
Usage
Barriers
Adoption
Intention
.370(.306)**
-.923(-.324)**
-.712(-.269)** (R2=.382)
Brand Concept
-.117(-.276)*
-.118(-.259)*
FIGURE 2 Empirical results for Study 2.
*Significant at 0.05 level; ** Significant at 0.01
level. Values in brackets indicate standardized
regression coefficients
CASIDY ET AL.
|
11
barriers (α= 0.88) related to AVs and indicated their intentions to
adopt this radical innovation (α= 0.93).
4.5.2 |Results and analysis
CFA was conducted to test construct validity. As indicated in Table 3,
the indicator loadings of each construct were statistically significant
and greater than the cutoff value of 0.50. Each construct showed
good reliability and convergent validity as reflected in the CRs >
0.70 (Bagozzi & Yi, 1988). Further, as shown in Table 4, Panel C, the
square root of AVE for each construct exceeds the correlations be-
tween variables, thus confirming discriminant validity
To test the focal hypotheses, we employed a moderated med-
iation analysis (PROCESS Model 7) following the procedure re-
commended by Hayes (2017). In this model, self‐brand connection
was the predictor, risk and usage barriers were the mediators,
marketing communication (0 = Low innovation capability; 1 = High
innovation capability) was the moderator, and adoption intentions
were the dependent variable. The manipulation check revealed that
participants in condition 1 rated the brand higher in terms of in-
novation capability than those in the low innovation capability con-
dition (MInnovationCapability
LowBI
= 4.37 < MInnovationCapability
HighBI
= 5.59, F(1,573) = 44.46, ρ< 0.001).
The findings replicate those of Studies 1 and 2, suggesting that
self‐brand connections are positively associated with intentions to
adopt the radical innovation (β= 0.30, t= 8.32, p< 0.001). More im-
portantly, the mediated effect of self‐brand connection on adoption
intentions via risk barriers (β
indirect
= 0.06, 95% CI = 0.0194–0.1080])
was significant when the innovation capabilities were communicated,
but not in the condition in which low innovation capabilities were
communicated (β
indirect
=−0.01, 95% CI = −0.0463 to 0.0343]). The
index of moderated mediation was significant (β
risk
= 0.11, 95%
CI = 0.0091–0.1300). Similarly, the indirect effect of self‐brand con-
nection on adoption intentions via usage barriers (β
indirect
= 0.03,
90% CI = 0.0015–0.0538) was marginally significant for the high in-
novation capability condition, but not in the low innovativeness
condition (β
indirect
=−0.01, 90% CI = −0.0391 to 0.0283). The index of
moderated mediation was significant (β
Usage
= 0.11, 95%
CI = 0.0025–0.0904). The findings thus provide support for H4.
Figure 3summarizes the empirical findings of Study 3 (detailed re-
sults of the analysis with the control variables can be found in
Table A4 in the Appendix).
4.5.3 |Discussion of results
The results of Study 3 further strengthen the validity of findings from
Studies 1 and 2 and, more importantly, provide support for our
proposition that conservation brands can also leverage consumer'
self‐brand connections when they increase their perceived innova-
tion capabilities through the use of marketing communications (H4).
Specifically, the findings show that even for conservation brands,
self‐brand connections can function as an important vehicle to re-
duce risk barriers, but only when consumers are reminded of the
brands' high innovation capabilities. The findings suggest that while
conservation brands might not be able to leverage their existing
customers' self‐brand connection as effectively as openness brands,
brand communications, particularly regarding the firm's innovation
capability, can help to strengthen the effects of self‐brand connec-
tion in reducing risk and usage barriers of adopting a radical
innovation.
5|DISCUSSION
In this study, we draw on theory related to innovation resistance and
brand management to: (1) investigate whether and how self‐brand
connection influences radical innovation adoption by mitigating risk
and usage barriers; (2) determine the moderating role that brand
concepts (conservation vs. openness) play in the aforementioned
chain of effects; and (3) examine how firms can communicate their
innovation capabilities to overcome consumer resistance to adopting
a radical innovation in the AV context.
Study 1 identified the risk and usage barriers associated with
resistance to AV, and then demonstrated how strong (vs. weak)
Self-Brand
Connection
Risk
Barriers
Usage
Barriers
Adoption
Intention
.299(.287)**
-.886(-.327)**
-.524(-.218)** (R2=.325)
Innovation Capability
-.075(-.196)*
-.080(-.185)*
FIGURE 3 Empirical results for Study 3.
*Significant at 0.05 level; ** Significant at 0.01
level. Values in brackets indicate standardized
regression coefficients
12
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CASIDY ET AL.
self‐brand connection reduces risk barriers, resulting in overall higher
radical innovation adoption. Study 2 tested the role of brand concepts
in moderating the effects of self‐brand connection on radical innovation
adoption. Specifically, it was shown that self‐brand connection is less
(vs. more) effective in reducing risk and usage barriers for brands with
conservation (vs. openness) concepts. Finally, Study 3 demonstrates
that conservation brands can communicate their innovation capability
to optimize and strengthen the mediated relationship between self‐
brand connections and adoption intentions.
5.1 |Theoretical implications
By examining the role of self‐brand connection as a means of overcoming
consumer resistance to radical innovation, this study advances our un-
derstanding of the interplay between brand management and innovation
resistance (Brexendorf et al., 2015). AVs represent a major technological
breakthrough involving scientific principles that are significantly different
from existing technology (i.e., platform innovation; Sood & Tellis, 2005).
More specifically, previous studies on the linkage between brand and
innovation management have primarily investigated how brands can
promote or hinder the success of innovation at firm or market level
(Beverland et al., 2010; Gill & Lei, 2009), with little attention devoted to
how brands can be leveraged to reduce consumer resistance to radical
innovation (Brexendorf et al., 2015). Furthermore, we have followed the
call to investigate “additional boundary conditions or moderators to the
branding strategy–consumer response relationship”in the context of
consumer resistance to radical innovations (Truong et al., 2017 p. 90).
The present study contributes to the branding and innovation
literatures in several ways. First, our findings suggest that besides
commonly investigated antecedents of innovation evaluation, such as
consumers' predispositions (Heidenreich & Spieth, 2013) and pro-
duct attributes (Joachim et al., 2018), self‐brand connection is an
important antecedent of innovation resistance. In light of incon-
clusive evidence (Truong et al., 2017), our study adds to the evidence
by demonstrating that cognitive and emotional connection between
a consumer and a brand influences the perceptions of risk associated
with radical innovation. In this way, we also extend previous research
on innovation (Claudy et al., 2015) by demonstrating that the re-
lationship between innovation resistance and radical innovation
adoption not only is determined by innovation characteristics, but
also is contingent on the brand factor, particularly the degree to
which consumers develop a sense of connection with the brand.
Second, our findings contribute to the growing literature on the
role of branding in overcoming consumers' resistance to innovation
(Heidenreich & Kraemer, 2016). Prior studies suggest that risk per-
ceptions have an affective component (Loewenstein et al., 2001), and
our findings reveal that the strong cognitive and emotional connec-
tions between consumer and brand translate into lower risk per-
ceptions. The critical mediating role of risk barriers found in this
study supports previous findings that have highlighted the im-
portance of the perceived level of risks as a barrier to adopting AI
technology (Davenport et al., 2020; Dwivedi et al., 2019). However,
our findings suggest that overcoming the usage barrier is challenging,
even when consumers have strong connections with the brands. Our
findings are aligned with a recent study in consumer research (Faraji‐
Rad et al., 2017) that identified the desire for control as a prominent
barrier to adoption of AI‐powered technology. Indeed, the emer-
gence of AI‐powered technology such as AVs would diminish one's
sense of control over the car and the environment due to the in-
herent uncertainty associated with AI (Kanal & Lemmer, 2014),
which is perceived as a threat.
Secondly, we identify and test an important boundary condition
to the mediated relationship between self‐brand connections and
innovation resistance (Truong et al., 2017) Prior studies have found
that the effect of consumers' predispositions (i.e., passive innovation
resistance) on innovation adoption is contingent on several moder-
ating variables (e.g., the perceived level of arousal, degree of new-
ness; Heidenreich & Spieth, 2013). Our findings further add to this
contingency perspective by demonstrating that the brand concept
plays an important role in moderating the effects of self‐brand
connection on consumer resistance to radical innovations. Specifi-
cally, our results indicate that for openness brands, which are per-
ceived as exciting and creative, the mitigating effect of self‐brand
connection on risk barriers is much stronger compared to con-
servation brands, which are characterized by tradition and con-
formity. Conservation brands should thus carefully consider whether
launching radical innovations under existing brand names is likely to
reduce risk perceptions and consumer resistance. However, findings
from Study 3 suggest that even conservation brands can leverage
self‐brand connections to overcome resistance to radical innova-
tions, when they communicate strong innovation capabilities to po-
tential adopters. In this way, our findings highlight an important
strategy that conservation brands can employ to leverage their
brands when launching and marketing radical innovation in con-
sumer markets.
Finally, we advance the literature on AI by highlighting the role
of brands in the diffusion of AI technology in the context of fully AVs.
Even though numerous car manufacturers are actively utilizing AI
technology, few studies have attempted to understand consumer‐
and firm‐specific factors that may affect the diffusion of fully AVs in
the future. While previous studies (Kaur & Rampersad, 2018;
Kyriakidis et al., 2015) identified some product‐related factors that
may hinder adoption, we contend that cars are primarily lifestyle
products, and those purchase decisions are strongly influenced by
brand‐related factors. Yet the role of brands has featured very little
in the debate on the adoption and diffusion of AVs. Our research has
addressed this gap by investigating the role that brands play in
overcoming consumer resistance to adopting AVs.
5.2 |Managerial implications
Our findings have important managerial implications. First, our study
has identified important risk and usage barriers that consumers as-
sociate with AVs. The findings highlight that consumers are primarily
CASIDY ET AL.
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13
concerned with the safety of self‐driving cars, and specifically with
AVs' ability to navigate dangerous situations (risk barriers). Fur-
thermore, consumers are concerned about the reduced joy of driving
and diminished feelings of freedom (usage barriers) associated with
AVs. While both barriers are negatively associated with lower
adoption intentions, our findings show that brands play a critical role
in reducing risk barriers. Specifically, consumers who report strong
connections with a brand associate lower risk barriers with AVs and
subsequently report higher adoption intentions. The findings suggest
that brands play a critical role in gaining consumers' trust and in
mitigating risk concerns that consumers may experience regarding
radically new technology such as AVs. Brand and innovation man-
agers should thus continue to leverage their brands when commu-
nicating the safety features of AVs while launching fully AVs in the
future.
On the other hand, brands will still have to overcome per-
ceived usage barriers and convince consumers that fully AVs can
offer as much joy and freedom (if not more) compared to tradi-
tional cars. Potential disruptions to established usage patterns
and the desire to be in control have dominated the marketing
landscape in the automotive industry for many years, and these
still seem to be the main reasons behind people's resistance to
AV adoption. Thus, marketing managers will have to convince
consumers that the use of AVs is in line with that of traditional
cars and that the autonomous function of the car exists to help
consumers enhance their driving experience rather than mini-
mizing their control over their vehicles.
Furthermore, our findings show that the positive effect of
strong self‐brand connection on adoption intentions is not equal
for all brand types. Specifically, our findings suggest that brands
with conservation concepts are likely to be at a disadvantage
compared to brands that score higher on the openness dimen-
sion. In other words, consumers appear to trust openness brands
more than conservation brands in terms of risk perception as-
sociated with AVs. However, this does not mean that car manu-
facturers with more conservation‐oriented concepts cannot
leverage their brands to overcome innovation resistance to
adopting a radical innovation. Our study demonstrates that even
brands with conservation concepts can leverage the positive ef-
fect of self‐brand connection if they strengthen perceived in-
novation capability through effective brand communications. We
do not recommend that conservation brands alter their brand
concepts, as this could alienate existing consumers. Rather,
brands with conservation concepts could launch campaigns that
would inspire confidence in their innovation capabilities. For in-
stance, they could aim to maintain this positioning at a lifestyle
level, while gaining innovation capability by, for example, show-
casing their innovative capabilities more aggressively. For ex-
ample, luxury car manufacturers such as Bentley and Maserati
have managed to strike a balance between tradition at the brand
level and innovation at the product level, to instill consumer
confidence in their ability to continuously advance technological
innovation (Rauwald & Sachgau, 2019).
5.3 |Limitations and future research
While this study has extended our knowledge about the factors that
will influence resistance to AVs, we acknowledge several limitations
that provide avenues for further research. First, the cross‐sectional
nature of the data limits the generalizability of the findings. Although
the link between the constructs found in this study is grounded in
established theoretical frameworks (Claudy et al., 2015; Escalas &
Bettman, 2003), the use of longitudinal data would strengthen the
validity of the study findings. Further, we acknowledge the limita-
tions of using MTurk and Prolific Academic to recruit respondents
for this study. Although we have employed several measures to
ensure quality data, there could be potential issues such as character
misrepresentation and selection bias (Goodman & Paolacci, 2017),
which may affect the integrity of the data. Our samples are also
limited to US consumers. Especially in regard to brands, cultural
differences might play an important role with respect to radical in-
novation adoption since AV manufacturers are located in many dif-
ferent regions. Future studies could address these limitations by
examining actual adoption behavior using market data across various
product categories involving a broader range of respondents beyond
US consumers to also assess cultural differences.
Second, while we focused on a selection of mainstream car
brands in our study, we recognize that type of firms may have con-
founding effects on our model. For example, nonconventional firms
such as Apple, Google, and Uber that are currently investing in the
development of AV technology may command a high degree of
consumers' self‐brand connection. However, because those firms are
not known for their expertise in the automotive industry, self‐brand
connection may not reduce usage and risk barriers for these brands.
An investigation of consumers' attitudes to AVs launched by these
nonconventional firms would be a valuable extension to the present
study. Future studies could also explore the influence of co‐branding
strategies (e.g., Toyota and Uber) and brand trust on consumers'
willingness to adopt AV. Because certain brands such as Apple and
Google command a high level of trust among consumers, brand trust
may moderate the effects of self‐brand connection on radical in-
novation adoption.
Third, while we control for relevant demographic characteristics of
age, gender, and income in our model (see Appendix Afor regression
results with demographic covariates), we recognize other possible con-
founding variables, such as existing knowledge structure, influencing the
relationship between key constructs in our model. For example, those
with substantial knowledge about AVs may perceive lower usage and risk
barriers associated with AVs. Thus, self‐brand connection, brand concept,
and innovation capability may play a lesser role for those who are highly
familiar/knowledgeable about the technology. Future studies could con-
sider consumer knowledge structure in examining the relationship be-
tween brands and radical innovation adoption.
Finally, our study focuses on AVs as a prime example of a radical
innovation involving a major technological breakthrough (i.e., platform
innovation). However, the effects of self‐brand connection on innovation
adoption may vary across different types of innovation (e.g., component
14
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CASIDY ET AL.
innovation and design innovation; Sood & Tellis, 2005). Indeed, some
studies confirmed that the degree of newness influences the strength of
innovation resistance and the corresponding effects on innovation
adoption (Heidenreich & Kraemer, 2015; Heidenreich et al., 2016). Fur-
thermore,Joachimetal.(2018) suggest that the relative importance of
risk and usage barriers varies between product and service innovations.
Hence, future research might test the applicability of our research model
across product versus service innovation or component versus design
innovation to shed light on potential differences between these types of
innovation.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from
the corresponding author upon reasonable request
ORCID
Riza Casidy https://orcid.org/0000-0002-6836-245X
Marius Claudy https://orcid.org/0000-0001-7214-1687
Sven Heidenreich https://orcid.org/0000-0003-2278-0610
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Marketing,1–21. https://doi.org/10.1002/mar.21496
APPENDIX A
A.1 | Elicitation of context‐specific risk and usage barriers
Prior research on innovation resistance has identified many barriers
that can potentially affect radical innovation adoption (see
Laukkanen et al., 2007; Lian & Yen, 2013). Furthermore, prior re-
search has confirmed that the importance of such barriers is highly
context‐dependent (Claudy et al., 2015; Joachim et al., 2018). We
therefore conducted a pre‐test to identify the barriers that are most
relevant in the context of AVs.
In an open‐ended survey, 115 MTurk respondents (M
age
= 34.88,
SD = 9.97; 39 female; median income $30,000–$39,999) were given
the autonomous driving technology product description and asked to
give reasons against the adoption of such technology. Following the
protocol established for qualitative data analysis (Glaser & Strauss,
2009; Spiggle, 1994), three researchers independently coded and
categorized the responses. More specifically, the three researchers
used established frameworks of commonly known risk and usage
barriers (Claudy et al., 2015; Talke & Heidenreich, 2014) to cate-
gorize, compare, and integrate statements pertaining to one or more
barriers using the qualitative data interpretation operations re-
commended by Spiggle (1994). Statements that could not be directly
assigned to the framework were categorized as “other reasons.”
Following this iterative process, 63% of the stated reasons could be
categorized as risk (47%) and usage barriers (53%). Consistent with
the literature (Salonen, 2018; Shariff et al., 2017) regarding risk
barriers, consumers were mainly concerned about safety issues and
the possibility of technological malfunction associated with AVs. In
terms of usage barriers, consumers were mainly concerned about the
loss of the “freedom”and “joy”of driving, which is consistent with
recent studies on consumer resistance to adopting autonomous
technology (de Bellis & Johar, 2020; Schweitzer & Van den
Hende, 2016).
In the next step, we took these findings and translated the
open‐ended answers into scale items that we refined and vali-
dated with a sample of MTurk participants (M
age
= 39.61,
SD = 12.22; 139 female; median income $50,000–$59,999).
To operationalize the risk and usage barriers construct, we
adapted 10 items based on established risk and usage barrier
items from the literature to our research context (Claudy et al.,
2015;Joachimetal.,2018) and applied 5‐pointLikertscales,
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CASIDY ET AL.
ranging from “astrongreason”to “not a reason at all,”to all
items. We conducted exploratory factor analysis (EFA) to further
establish convergent validity of the risk and usage barriers con-
struct. We performed the Kaiser–Meyer–Olkin test (total matrix
sampling adequacy = 0.90) and Bartlett's test of sphericity on the
measures (approx. Χ
2
= 1478.76, df = 45; p< 0.001) to provide
evidence that our data were adequate for EFA (Table A1).
Principal component analysis with varimax rotation was
employed on all items. The results revealed the presence of two
factors with eigenvalues exceeding 1, explaining 63.98% of the
total variance (Table A2). As seen in Table A2,fiveitemsshow
substantial factor loadings on Factor 1 (>0.50), with little cross‐
loading (<0.30) on Factor 2. All the items in Factor 1 related to
the risk barriers of adopting autonomous car technology. Three
items show substantial factor loadings on Factor 2 (>0.50) with
little cross‐loading (<0.30) on Factor 1. All the items in Factor 2
relate to the usage barriers of adopting autonomous car tech-
nology. The items with high cross‐loadings (>0.30) are subse-
quently removed from further analysis in our main studies.
Importantly, both the risk barrier (α= 0.86) and usage barrier
(α= 0.79) constructs exhibited high internal consistency.
A.2 | Study 2: Identification of conservation versus openness
brands
To manipulate and test the moderating roles of brand concept,
wefollowedthesameproceduresasthoseofTorellietal.(2012)
and first identified real brands with high scores for conservation
(vs. openness). We conducted two pre‐testsforStudy2.Thefirst
pre‐test was conducted to identify the brands with which re-
spondents are most familiar; the second pre‐test was conducted
to examine which brands score highest in the openness and
conservation dimensions.
In pre‐test 2a, we recruited 552 US respondents (M
age
=
36.89, SD = 12.28; 295 female; median income $50,000–$59,999)
from Prolific Academic. We pooled 40 car brands based on the
top‐40 automotive sales figures in the US market (ANDC, 2017).
Each respondent was asked to rate seven car brands in
terms of his/her familiarity with the brand. In pre‐test 2b, 241
US respondents (M
age
= 31.35, SD = 11.70; 104 female;
$50,000–$59,999) from Prolific Academic were recruited to
evaluate 14 car brands based on the most familiar brands iden-
tified in pre‐test 2a. Each respondent was randomly allocated to
three car brands and was asked to rate each brand in terms of
TABLE A1 Variance explained for risk and usage barriers
Initial eigenvalues Extraction sums of squared loadings Rotation sums of squared loadings
Component Total % of variance Cumulative % Total % of variance Cumulative % Total % of variance Cumulative %
1 5.178 51.776 51.776 5.178 51.776 51.776 3.854 38.538 38.538
2 1.220 12.203 63.979 1.220 12.203 63.979 2.544 25.441 63.979
3 0.928 9.280 73.260
4 0.541 5.415 78.674
5 0.472 4.724 83.399
6 0.443 4.431 87.830
7 0.360 3.605 91.434
8 0.300 3.001 94.435
9 0.285 2.851 97.286
10 0.271 2.714 100.000
Note: Extraction method: principal component analysis.
TABLE A2 Rotated component matrix for risk and usage
barriers
Component
12
Because the technology might not react correctly in
ambiguous situations
0.858 0.171
Because the technology could never predict other
human behavior
0.823 0.172
Because the technology can never replace human
judgment in dangerous situations
0.788 0.265
Because of the possibility of technological failure or
malfunction
0.711 0.283
Because I trust my own driving ability more than
the technology
0.643 0.432
Because the technology is not safe 0.641 0.167
Because it reduces the joy of driving 0.881
Because it limits my feeling of freedom when
driving
0.822
Because I do not want to give up control 0.584
Because I might become overly reliant on the
technology
0.325 0.572
CASIDY ET AL.
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openness (four items: daring, exciting, freedom, and creativity;
α= 0.91) and conservation (four items: tradition, conformity,
security, and self‐discipline; α= 0.80) brand concepts using
7‐point Likert scales adapted from Torelli et al. (2012). As shown
in Table A3,amongtheUS‐based car manufacturers, Tesla was
rated highest on the openness concept (M= 5.62), while Ford was
rated highest in terms of conservation (M= 4.61). Both brands
also have high levels of brand familiarity (MFamiliarity
Tesla
=5.06,
MFamiliarity
Ford
= 5.78), comparable to other brands included in
the pre‐test. An independent sample t‐test reveals that Tesla and
Ford are significantly different in terms of the openness dimen-
sion (MOpenness
Tesla
=5.62>MOpenness
Ford
= 3.61, t=8.02,
p< 0.001) and the conservation dimension (MConserva-
tion
Tesla
=3.23>MConservation
Ford
= 4.61, t=−5.88, p<0.001).
Hence, both brands were used in Study 2 as stimuli for the re-
spective brand concept dimension, allowing us to test H3.
A.3 | Study 3
A.3.1 | Scenario for high (vs. low) innovation capability
for Study 3
Below is a snippet from a recently published newspaper article.
Please read the article and answer the questions on the
next page.
A.3.2 | High innovation capability
In its March 4, 2016, edition, Fortune magazine presented the latest
results of its annual “Most Innovative Car Brands“Innovation Survey
(pp. 75–82). Fortune asked over 1000 executives, directors, and
analysts to rank all major car brands on key aspects of its innovation.
Toyota was ranked as the second most innovative car brand in the
world, a rapid rise from sixth place just 3 years ago. Results for the
car brands are as follows:
(Reputation for innovation, where 1 = “most innovative”and
10 = “least innovative”):
1. Tesla = 8.81
2. Toyota =8.43
3. Audi = 6.95
4. BMW = 5.35
5. Porsche = 4.43
6. General Motors = 3.65
A.3.3 | Low innovation capability
In its March 4, 2016, edition, Fortune magazine presented the latest
results of its annual “Most Innovative Car Brands“Innovation Survey
(pp. 75–82). Fortune asked over 1000 executives, directors, and
analysts to rank all major car brands on key aspects of its innovation.
Toyota was ranked as the sixth most innovative car brand in the
world, a rapid decline from second place just 3 years ago. Results for
the car brands are as follows:
(Reputation for innovation, where 1 = “most innovative“and
10 = “least innovative”):
1. Tesla = 8.81
2. General Motors = 8.43
3. Audi = 6.95
4. BMW = 5.35
5. Porsche = 4.43
6. Toyota =3.65
A.3.4 | Analysis with control variables
Table A4 reveals the results of our analyses with the inclusion of all
control variables (age, gender, and income) across all studies. The
results reveal consistent effects of self‐brand connection on adop-
tion intentions across all studies with the inclusion of demographic
variables as covariates.
TABLE A3 Study 2 (openness vs. conservation brands)
Openness Conservation Familiarity
Audi 4.61 4.00 4.72
BMW 4.84 3.95 4.88
Buick 3.25 4.23 4.27
Chevrolet 3.74 4.47 5.42
Chrysler 3.48 4.27 4.53
Ford 3.61 4.61 5.78
GMC 3.48 4.26 4.62
Jaguar 4.97 4.09 4.35
Lexus 4.36 4.10 4.81
Maserati 5.22 3.32 3.92
Porsche 5.17 3.88 4.79
Tesla 5.62 3.23 5.06
Toyota 4.17 4.83 5.82
Volvo 3.95 4.44 4.60
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TABLE A4 Regression with control
variables DV: Adoption intentions
Study 1 Study 2 Study 3
Predictor βtpβtpβtp
Self‐brand
connection
0.374 7.470 0.001 0.380 6.578 0.001 0.301 8.452 0.001
Age −0.013 −2.041 0.042 −0.026 −3.572 0.001 −0.015 −3.137 0.002
Gender −0.199 −1.283 0.201 −0.369 −2.156 0.032 −0.360 −3.183 0.002
Income 0.013 0.475 0.635 0.028 1.181 0.239 0.003 0.170 0.865
CASIDY ET AL.
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